What's up everyone, today we have the pleasure of sitting down with Alex Halliday, Founder and CEO at AirOps.

(00:00) - Intro
(01:19) - In This Episode
(01:54) - Sponsor: Attribution App
(02:57) - Sponsor: GrowthLoop
(04:19) - How AirOps Pivoted to AI Content Engineering
(08:23) - The Real Definition of Content Engineering and Why It's Not About Publishing More
(13:14) - What a Content Engineer Does That a Senior Content Marketer Does Not
(27:31) - What It Actually Takes to Get AI Content Past a Human Editor
(30:52) - Sponsor: Knak
(32:00) - Sponsor: MoEngage
(43:21) - Why Review Becomes the Bottleneck After You Automate Content Production
(47:13) - Why Enterprise CMS Integration Is Harder Than the Content Quality Problem
(51:07) - Why the Agent Runtime Is the Next Competitive Battleground for Content Teams
(55:02) - What the Case Against Content Engineering Gets Wrong About the Role
(58:08) - What a Content Engineering Team Looks Like in 3 Years
(01:03:45) - How Alex Decides What Deserves His Energy

Summary: Alex built AirOps to help teams access company data, then a conversation with Sam Altman and a cramped middle seat on a flight to Atlanta changed everything. In this episode, he breaks down what content engineering actually means — not just generating more AI content, but building the systems infrastructure to maintain quality, freshness, and brand accuracy across everything a company has ever put online. He makes the counterintuitive case that great content engineering puts more humans into the content process, and explains why 98% of AirOps's pilots convert to annual customers while most AI content pilots fail. If you think AI content is just a faster way to publish more, this episode will change how you think about it.

About Alex Halliday

Alex Halliday is the Founder and CEO of AirOps, where he leads the development of AI content engineering systems that help brands build visibility in AI search. Before founding AirOps in 2022, he served as Head of Product at MasterClass, where he was the company's first product hire and helped scale revenue 10x. As a Venture Partner at SparkLabs Global Accelerator, Alex has made early investments in OpenAI, Anthropic, Groq, and Discord.

How AirOps Pivoted to AI Content Engineering

In early 2022, the LLM moment hadn't happened yet. Not publicly. GPT-3 existed but was barely on anyone's radar in marketing. Most "AI for marketing" conversations were still about sentiment analysis tools and basic chatbots. The prevailing assumption was that software had rules, rules had limits, and those limits were the floor you designed around.

Alex Halliday had an unusual vantage point. As a venture partner at SparkLabs Global Accelerator with early investments in OpenAI and Anthropic, he was closer to what was actually happening than almost anyone in his world. He still wasn't ready for what came next.

It started with a conversation. He was in San Francisco with Sam Altman, something he made a habit of — whenever they crossed paths, Alex asked the same question: what's sparking your imagination these days? On this particular occasion, Altman's answer was different. The AI stuff was getting really good, he said. When Alex pushed for specifics, Altman told him they were getting close to AI that could read all your emails and tell you what to do for the week. It sounded completely insane.

Alex filed it away. Then, a few weeks later, he was on a flight to Atlanta, sandwiched in the middle seat between 2 large men with nowhere to go and nothing else to do. He finally opened an OpenAI account and started building.

That experience in a cramped middle seat sent AirOps in a new direction. The company had been founded to help non-technical employees access company data — a broad, useful product with no obvious north star. Knowing the paradigm was shifting and knowing what your company should actually do about it are different problems. Alex had to translate that conviction into a focus, which meant making a hard call. When a space is growing as fast as LLM applications were in 2022 and 2023, trying to be everything to everyone is a trap.

The answer came from the data, not from a whiteboard. When the team looked at their heat map of usage, 1 cluster burned hotter than anything else: technical CMOs, leaders of 50 to 100 person marketing orgs, working nights and weekends inside AirOps building ambitious content systems. High-taste users with strong opinions and no patience for tools that couldn't meet their standard. The market was doing what markets do when they find something they want — it was insisting.

By mid-2023, AirOps had committed fully. The customer was the high-taste marketing professional who wanted to build content systems at scale, not just generate more content. Every decision since has been built around that person. The most important pivots rarely happen in planning sessions. They happen when you actually use the thing, look at the data honestly, and trust what the market is telling you over the story you had planned to tell.

Key takeaway: Look at your usage data and find the cluster of users who are working hardest and complaining most specifically — they are telling you who your product is actually for. Make time to try the tools reshaping your industry with your own hands. Alex's pivot started in a cramped middle seat he couldn't escape. Any open hour will do.

The Real Definition of Content Engineering and Why It's Not About Publishing More

Marketing teams have been chasing the wrong metric since LLMs went mainstream. The race defaulted to volume: how many posts, how fast, how much can you automate. That framing made sense in an era where more content meant more crawlable pages, more keywords, more surface area for Google to index. The era has changed.

AI agents now sit between buyers and brands. When someone asks ChatGPT or Perplexity a question about your product category, an agent synthesizes content from across the web — your owned pages, third-party publications, Reddit threads, review platforms — and returns a single answer. That agent is not counting pages. It's evaluating quality, depth, freshness, and what Alex describes as information gain: the degree to which any given piece of content adds something new to what the model already knows.

That's a meaningfully different standard. A 2022 blog post with outdated product language, stale statistics, and broken links doesn't rank lower in AI search — it's absent from it entirely. Webflow, 1 of AirOps's customers, saw what investing in content refresh workflows does to those outcomes: 42% more traffic and AI-attributed conversions performing 6x better than standard organic. That's a maintenance story, not a content production story.

There's also a conflation doing a lot of damage in this conversation. Content written with AI assistance gets lumped together with content generated by AI with no original grounding or context. The studies that say "AI content performs poorly" tend to define AI content as the second category, and the conflation goes unexamined in most LinkedIn commentary. The distinction matters enormously. Content that draws on real interviews, proprietary data, internal expertise, and company-specific context performs differently from content that's a model recombining what already exists on the internet.

The brands performing well in AI search right now are treating their content library as a living system with real quality standards — a garden that requires ongoing maintenance rather than a publishing archive. They're building workflows to keep content fresh, surface internal knowledge that's been sitting in Google Drive unused, and maintain what...